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Digital Twin was introduced over a decade ago, as an innovativeall-encompassing tool, with perceived benefits including real-time monitoring,simulation and forecasting. However, the theoretical framework and practicalimplementations of digital twins (DT) are still far from this vision. Althoughsuccessful implementations exist, sufficient implementation details are notpublicly available, therefore it is difficult to assess their effectiveness,draw comparisons and jointly advance the DT methodology. This work explores thevarious DT features and current approaches, the shortcomings and reasons behindthe delay in the implementation and adoption of digital twin. Advancements inmachine learning, internet of things and big data have contributed hugely tothe improvements in DT with regards to its real-time monitoring and forecastingproperties. Despite this progress and individual company-based efforts, certainresearch gaps exist in the field, which have caused delay in the widespreadadoption of this concept. We reviewed relevant works and identified that themajor reasons for this delay are the lack of a universal reference framework,domain dependence, security concerns of shared data, reliance of digital twinon other technologies, and lack of quantitative metrics. We define thenecessary components of a digital twin required for a universal referenceframework, which also validate its uniqueness as a concept compared to similarconcepts like simulation, autonomous systems, etc. This work further assessesthe digital twin applications in different domains and the current state ofmachine learning and big data in it. It thus answers and identifies novelresearch questions, both of which will help to better understand and advancethe theory and practice of digital twins.

Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions